Instructor: Anand Bhattad  (bhattad -at- ttic.edu)
Lectures: T TH 2:00-3:20, 530 TTIC

Practice Presentation with Instructor:
M 1-2:30PM (for TH Presentations), 509 TTIC
F 1-2:30PM (for T Presentations), 509 TTIC

Office hours: F 3:30-4:15 PM or by appointment, 509 TTIC

Always check announcements on Slack for short-notice changes to instructor office hours!

Contents: requirements, topics & schedule, acknowledgement, resources

Overview

In the words of Charles Dickens, "It was the best of times, it was the worst of times..." The ever-evolving field of computer vision embodies this sentiment, generating both excitement and looming concern--the worry that the richness of the field's history, predating the "deep learning revolution," might be overlooked by newcomers. While the deep learning revolution has brought unprecedented progress, it's also important not to overlook the rich history that preceded it.

This graduate seminar course aims to bridge the gap and connect the dots between the surge in deep learning and the foundational work that laid the groundwork. While not all historical research directly contributed to the latest methods, overlooking the field's evolution risks missing out on essential insights. We will revisit seminal works still relevant today alongside modern approaches, identifying the most impactful ideas over time and exploring past lessons that continue to influence the future.

The course is organized around student presentations, with the first few lectures by the instructor setting the tone. The class will cover a span of topics, from low-level vision to generative models. Each session will discuss two papers -- one historical (pre-2009) and one modern (post-2010) -- to highlight the field's evolution. Beyond presentations, students will engage in a project that revisits old ideas with modern tools, offering a unique perspective in today's fast-paced research environment. This course is not just a retrospective; it's an opportunity to gain a fresh outlook on computer vision. Requirements include two presentations, a final project, peer grading, and active participation.

Topic List and Papers

• Presentations: 60%
• Group project: 20%
• Peer grading reports: 10%
• Participation: 10%

• Signup: Shortly after the first lecture on Tuesday, March 19th, an email will be sent to all registered students with a link to the signup sheet. This link will also be posted on Slack. Topics are assigned on a first-come, first-served basis. However, if no one signs up for some topics, I may ask some students to switch to ensure coverage. Signup deadline for registered students is 2PM on March 21st. If you fail to sign up promptly, you will lose 10% of your presentation grade.

  After the deadline for registered students, any unregistered students interested in taking the course or auditing the course are free to sign up for the remaining spots. The presentation schedule will be finalized by the end of Friday, March 22nd.

  Keep in mind that the act of signing up for a topic is a commitment to your instructor, and all the other students in the class. Therefore, if you are unsure whether you will stay in the course, we urge you to make this decision now if at all possible.
  

• Practice presentation (30% of presentation grade): Each student must do a practice presentation with Anand about three days before the actual presentation date. This session aims to provide feedback to ensure your in-class presentation meets the highest standards. The practice presentation doesn't need to be fully polished or complete, but grading will assess the seriousness of your preparation. There are two available slots on Monday (for Thursday presentations) and two on Friday (for Tuesday presentations), each lasting 45 minutes. Please contact Anand to schedule your practice presentation at least one week in advance for one of these slots. You are also encouraged to consult with Anand during office hours about your paper or draft slides, even before the formal practice session.
  
• Slides (30% of presentation grade): By the end of the day before your scheduled presentation, you must send Anand your slides in either PowerPoint or PDF format via Slack.These slides will then be made available to all students on the course webpage. Remember to include your names on the title slide. Failing to submit your slides on time will result in a loss of this portion of your grade. Additionally, please pay close attention to the guidelines on credit attribution below, as not adhering to them could negatively impact your grade for this section and might even be regarded as a violation of academic integrity.
  
• In-class presentation (30% of presentation grade): The presentations will be evaluated on several key criteria: clarity of delivery, technical depth of the content, successful synthesis of the discussed material, the presenter's ability to engage and involve the audience, and how well the feedback from the practice presentation was incorporated and responded to.
  
• Peer grading (10% of presentation grade): Each presentation will be evaluated by three to five of your classmates. Their individual scores will then be averaged to determine this portion of your overall grade. The peer grading form is here. For details, see below.
  

Guidelines for creating a successful presentation:

• There will be two papers to cover in each class. The first introduces the topic and covers the basics before getting into specifics, with a bonus for summarizing key developments from the historic to the modern paper in the last few slides. The second paper should relate back to the first, exploring how the two approaches might connect.
  
• Each presentation will last for 25 minutes, followed by 5 minutes for audience questions. The final 15 minutes of the class will be dedicated to a discussion led by me, based on questions about the papers submitted by students the day before.
  
• Think of yourself as a professor for the day. Aim to deliver a comprehensive and understandable lecture on a specific topic, ensuring the technical depth is appropriate for our audience.
  
• Be sure to place your topic in the context of the entire course. Whenever appropriate, take care to point out any specific connections to other presentations that came before.
  
• Where appropriate, feel free to bring a critical perspective to your topic. Go beyond simply describing the techniques. Question assumptions, expose possible flaws and limitations, suggest alternatives and/or directions for future research. Keep in mind that some of the papers you are covering are very recent, making skepticism about any extraordinary/unsubstantiated claims warranted.
  
• Be sure to involve the class. When you are developing your presentation, identify places where you can ask other students for input, or topics that you want to open up for discussion.
  
• Because timing is hard to predict, you need to maintain some flexibility in terms of the paper you will cover. It is a good idea to have one or two sections in the latter half of your slides that you can skip depending on the time. When you are presenting, keep an eye on the time and adjust the pacing towards the end accordingly.
  
• Use of external sources and credit attribution: Be sure to explicitly give credit whenever you use material from other sources. If you "borrow" any slides or graphics, be sure to give the original source in small font on the bottom of each slide. If you show a demo based on somebody's code, be sure to clearly announce this. Failure to follow these guidelines will hurt your score for the slides, and may even be considered an academic integrity violation. It is not acceptable to use an entire slide deck from another source "as is" as the basis for your presentation.

• Presentation Signup
• Presenters: Practice presentation is required three days before your actual presentation. Finalized slides are due the night before your presentation. Come to class at least five minutes early to make sure that your laptop works with the projector.
• Peer graders: Reports for Tuesday presentations are due via Slack to Anand by the end of Friday, and reports for Thursday presentations are due by the end of the following Sunday.

This class is inspired by Lana Lazebnik's Short Course: "Looking Back to Look Forward" at Georgia Tech in 2020. Her videos are available and viewing them is highly encouraged. The course follows the design, structure, policies and also the webpage template of another course by Lana Lazebnik: "Cutting-Edge Trends in Deep Learning and Recognition".

Special thanks to David Forsyth, David McAllister, Derek Hoiem, Greg Shakhnarovich, Lana Lazebnik and Shubham Tulsiani for their useful suggestions in designing this course.

Useful Resources

• Georgia Tech Looking Back to Look Forward course
• UIUC Computer Vision: What Will Stand Test of Time? course
• Berkeley Visual Scene Understanding course
• CMU Visual Learning and Recognition course
• UIUC 3D Vision course

Reading List

• Lana's Reading List from her Computer Vision: What will stand test of time class?